CN108988370A - The capacity determining methods of energy storage device, equipment and storage medium in electric system - Google Patents
The capacity determining methods of energy storage device, equipment and storage medium in electric system Download PDFInfo
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- CN108988370A CN108988370A CN201810961586.6A CN201810961586A CN108988370A CN 108988370 A CN108988370 A CN 108988370A CN 201810961586 A CN201810961586 A CN 201810961586A CN 108988370 A CN108988370 A CN 108988370A
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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Abstract
The invention discloses a kind of capacity determining methods of energy storage device in electric system, the target component being first depending in electric system determines multiple norm constraint parameters；Then each norm constraint parameter is optimized again；The capacity of energy storage device is finally determined using each norm constraint parameter after optimization.It can be seen that, the capacity determining methods are to be determined according to multiple norm constraint parameters after optimization to the capacity of energy storage device, compared with only choosing single standard constrained parameters in the prior art and being determined to the capacity of energy storage device in electric system unilaterally, using this determination method, the capacity accuracy that energy storage device can be improved, promotes the stable operation of electric system.In addition, the invention also discloses capacity determining device, equipment and the storage medium of energy storage device in a kind of electric system, effect is as above.
Description
Technical field
The present invention relates to electric power project engineering fields, in particular to capacity determining methods of energy storage device in electric system,
Equipment and storage medium.
Background technique
With the development of science and technology, renewable energy using more and more extensive, for example, being generated electricity using renewable energy
The transmission & distribution electric loss of electric system can be reduced.It needs to be determined that energy storage when being stored by energy storage device to each renewable energy
The capacity of device, the only random amount of capacity that energy storage device is determined using single standard constrained parameters general at present, due to choosing
There are one-sidedness for the single standard constrained parameters taken, so the capacity accuracy for eventually resulting in the energy storage device determined is low,
And then it will affect the stable operation of electric system.
It can be seen that how to overcome when being determined using capacity of the single standard constrained parameters to energy storage device, cause
The low problem of the capacity result accuracy determined be those skilled in the art's urgent problem to be solved.
Summary of the invention
The embodiment of the present application provides the capacity determining methods of energy storage device, equipment and storage medium in electric system, with
When solving in the prior art to be determined the capacity of energy storage device using single standard constrained parameters, the caused appearance determined
Measure the low problem of result accuracy.
In order to solve the above technical problems, the present invention provides a kind of capacity determining methods of energy storage device in electric system,
Include:
Multiple norm constraint parameters are determined according to the target component in electric system；
Each norm constraint parameter is optimized；
The capacity of energy storage device is determined using each norm constraint parameter after optimization.
It is preferably, described that each norm constraint parameter is optimized specifically:
The norm constraint parameter is optimized by shuffled frog leaping algorithm.
Preferably, each norm constraint parameter after the utilization optimizes determines after the capacity of energy storage device, further includes:
It is modified based on capacity of the ε-leash law to the energy storage device.
Preferably, the energy storage device specifically includes Wind energy storage device corresponding with wind generator system, sends out with photovoltaic
The corresponding solar energy storage device of electric system, chemical energy energy storage device corresponding with fuel cell, electrolytic cell and air accumulator.
Preferably, the norm constraint parameter includes loss of supply probability, total energy loss value and the institute of electric system
State the power difference between the capacity of the power supply device and the energy storage device in electric system.
In order to solve the above technical problems, the present invention also provides a kind of capacity determination sides with energy storage device in electric system
The corresponding device of method, comprising:
First determining module, for determining multiple norm constraint parameters according to the target component in electric system；
Optimization module, for being optimized to each norm constraint parameter；
Second determining module, for determining the capacity of energy storage device using each norm constraint parameter after optimization.
Preferably, the optimization module is specifically used for carrying out the norm constraint parameter by shuffled frog leaping algorithm excellent
Change.
Preferably, further includes:
Correction module, for being modified based on capacity of the ε-leash law to the energy storage device.
In order to solve the above technical problems, the present invention also provides a kind of capacity determination sides with energy storage device in electric system
The corresponding equipment of method, comprising:
Memory, for storing computer program；
Processor, for executing the computer program to realize the appearance of energy storage device in any one of the above electric system
The step of measuring the method for determination.
In order to solve the above technical problems, the present invention also provides a kind of capacity determination sides with energy storage device in electric system
Method corresponding computer readable storage medium is stored with computer program, the calculating on the computer readable storage medium
Machine program is executed by processor the step of to realize the capacity determining methods of energy storage device in any one of the above electric system.
Compared with the prior art, in a kind of electric system provided by the present invention energy storage device capacity determining methods, it is first
First multiple norm constraint parameters are determined according to the target component in electric system；Then each norm constraint parameter is carried out again excellent
Change；The capacity of energy storage device is finally determined using each norm constraint parameter after optimization.It can be seen that the capacity determining methods are
The capacity of energy storage device is determined according to multiple norm constraint parameters after optimization, is only chosen in the prior art single
Norm constraint parameter the capacity of energy storage device in electric system be determined unilaterally compare, can using this determination method
To improve the capacity accuracy of energy storage device, promote the stable operation of electric system.In addition, the present invention also provides a kind of electric power
Capacity determining device, equipment and the storage medium of energy storage device, effect are as above in system.
Detailed description of the invention
Fig. 1 is the capacity determining methods flow chart of energy storage device in a kind of electric system provided by the embodiment of the present invention；
Fig. 2 is the wind turbine output power of wind generator system and the relationship of wind speed provided by the embodiment of the present invention
Curve graph；
Fig. 3 is the output power curve figure of the Photovoltaic array in photovoltaic generating system provided by the embodiment of the present invention；
Fig. 4 is the output voltage of fuel cell and the graph of relation of current density provided by the embodiment of the present invention；
Fig. 5 forms signal for the capacity determining device of energy storage device in a kind of electric system provided by the embodiment of the present invention
Figure；
Fig. 6 determines equipment composition signal for the capacity of energy storage device in a kind of electric system provided by the embodiment of the present invention
Figure.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art without making creative work it is obtained it is all its
Its embodiment, shall fall within the protection scope of the present invention.
Core of the invention is to provide the capacity determining methods of energy storage device, equipment and storage medium in electric system, can
It is caused to determine when solving in the prior art to be determined the capacity of energy storage device using single standard constrained parameters
The low problem of capacity result accuracy.
Scheme in order to enable those skilled in the art to better understand the present invention, with reference to the accompanying drawing and specific embodiment party
The present invention is described in further detail for formula.
Fig. 1 is the capacity determining methods flow chart of energy storage device in a kind of electric system provided by the embodiment of the present invention,
As shown in Figure 1, the capacity determining methods the following steps are included:
S101: multiple norm constraint parameters are determined according to the target component in electric system.
In the prior art, when the capacity to the energy storage device in electric system is determined, a mark is only randomly selected
The capacity accuracy of quasi- constrained parameters, the energy storage device finally determined in this way is low, and will affect the stability of electric system.
The capacity determining methods of energy storage device in a kind of electric system provided by the embodiment of the present application, first according to electric power
Target component in system determines multiple norm constraint parameters.
Specifically, it is assumed that the objective function that the multiple norm constraint parameters determined are constituted are as follows:
min{f1(x),f2(x),…,fk(x)}
Wherein, x is decision vector, fi(i=1,2 ..., k) it is objective function.If fj(x), j ∈ { 0,1 ..., k } is to excellent
The objective function of change, then fi(x) it is goal constraint, is shown below:
Wherein S is solution space, and k is the number of objective function, εiIt is the binding occurrence of objective function.
Totle drilling cost Cost is chosen as fitness function, calculation formula are as follows:
Cost=Ci+∑PVr-i+∑PVOM-i+∑PVend-i
Wherein, CiRefer to overall cost of ownership, PVr_iRefer to system unit replacement cost, PVOM_iRefer to operation expense, PVend_i
Refer to the remaining value of system unit.
Firstly, collecting wind speed and solar radiation data per hour and per hour electric load, pass through wind speed and sun spoke
The output power of wind-power electricity generation and photovoltaic power generation can be determined by penetrating data, in conjunction with the maximum work of intermittent carry calculation this area
Rate.
Fig. 2 is the wind turbine output power of wind generator system and the relationship of wind speed provided by the embodiment of the present invention
Curve graph, as shown in Figure 2.Wind turbine output power depends primarily on three kinds of wind speed: incision wind speed (V incision), specified wind
Fast (V is specified) and cut-out wind speed (V is cut out).Wind turbine starts to cut capture power in V, and with the cubic function of wind speed
Increase, until reach V it is specified under rated power Pr.The output power of wind turbine keeps Pr, cuts until wind speed reaches V
Out.The wind speed cut out for being higher than V, it is necessary to stop wind turbine, prevent from damaging since wind-force is excessive.
The output power P of wind-driven generatorWTCalculation formula are as follows:
Wherein, VIncision、VIt is specified、VIt cuts outRespectively refer to incision wind speed, rated wind speed and the cut-out wind speed of wind turbine, PrFinger volume
Determine the rated output power of wind speed apparatus for lower wind turbine, the calculation formula of coefficient a, b are as follows:
Consider the actual height h of wind turbine and the height h of measuring pointm, the wind speed V of wind turbine and measurement are resulting
Wind speed VmRelationship be shown below:
Wherein, α refers to coefficient of friction, is the function for the landform that wind speed is blown over.In the embodiment of the present application, the α of open terrain
Value is 1/7 or 0.1429.
Photovoltaic generating system converts sunlight directly into electric energy.Fig. 3 is photovoltaic power generation provided by the embodiment of the present invention
The output power curve figure of Photovoltaic array in system, as shown in figure 3, the output characteristics of Photovoltaic array module be it is nonlinear,
This depends on sunlight, battery temperature and particular job point.For Photovoltaic array, ideal operating point at different conditions
It, can be by power electronic converter in its output end when being that output power is in its highest possible value (i.e. maximum power point)
Suitable control is realized.The output power P of Photovoltaic arrayPVCalculation formula are as follows:
PPV=η HA
Wherein, η refers to the efficiency of entire photovoltaic system, H (W/m2) refer to solar radiation, A (m2) refer to the gross area of photovoltaic module.
In present application example, the output power per hour of photovoltaic system is calculated using practical solar radiation per hour.More than if
Data can not obtain, and in practical applications, it is any in one day of what position can predict that the earth is taken up an official post using corresponding equation
The time sun on high in position, and calculate solar radiation accordingly.
In the embodiment of the present application, it is preferable that norm constraint parameter includes loss of supply probability, the gross energy of electric system
Power difference between penalty values and electric system and the capacity of energy storage device.That is, can be according in electric system
Target component determine the appearance of the loss of supply probability of electric system, total energy loss value and electric system and energy storage device
Power difference these three norm constraint parameters between amount.In practical applications, these three norm constraint parameters are known as target
Function.
Loss of supply probability LPSP refers to the ratio of energy defective value and energy demand value, calculation formula are as follows:
Wherein, DE refers to energy defective value, PloadRefer to energy demand value.In the embodiment of the present application, including wind-power electricity generation, light
The calculation formula of the energy defective value DE in polymorphic energy consumption system (electric system) including volt power generation and battery is as follows:
DE=Pload(t)×Δt-([PWT(t)+PPV(t)]×Δt+SOC(t-1)-SOCmin)×ηinv
Wherein, PloadIt (t) is electric load, PWT(t) be wind-driven generator output power, PPVIt (t) is photovoltaic power generation
Output power, SOC (t) are the current charged state of battery, SOCminIt is the minimum charge volume of battery, ηinvIt is converter efficiency.
The energy stored in the charged state SOC and system of battery is related, calculation formula are as follows:
SOC (t+1)=SOC (t) σ+Ibat(t)Δtη(Ibat(t))
Wherein, σ is self-discharge rate, IbatIt is charging current, η is charge efficiency.
Total energy loss value TEL refers to polymorphic energy consumption system additional power and the energy summation lost, calculation formula are as follows:
Wherein, EGIt is the gross energy that resource generates, LD is loading demand.In an example of the present invention, if polymorphic use
Energy system is in island state, then LD further includes the storage electric energy other than the electric energy of loading demand.
Power supply device in electric system and the power difference TELSUB between the capacity of energy storage device can occur at two kinds
In the case of.When the first happens the generated output in polymorphic energy consumption system greater than consumption, second situation generation is being sent out
When electrical power is less than consumption.In the first scenario, TELSUB includes two parts, first is that excessive generated output and electrolytic cell system
The difference of Hydrogen Energy power, second is that due to the waste of excess power caused by the low capacity and large scale of wind turbine or PV plate.
In the latter case,
TELSUB equally includes two parts, first is that the difference of electricity shortage and fuel cell power generation ability, second is that due to low
Load loss caused by energy storage capacity.
It in practical applications, can be by the power supply in loss of supply probability LPSP, total energy loss value TEL, electric system
Power difference TELSUB and accumulator capacity between device and the capacity of energy storage device is as constraint condition.Decision vector x is fixed
Justice is as follows,
X=[PPVr,PWTr,PFCr,Pelecr,Capbat,Mtank]
Wherein, PPVr、PWTr、PFCr、PelecrIt is photovoltaic generating system, wind generator system, fuel cell and electrolytic cell respectively
Rated power, CapbatAnd MtankIt is the capacity of battery and hydrogen container respectively.
Then totle drilling cost function is shown below:
Wherein, i is interest rate, Ci,jIt is capital cost, COM,jIt is operation and maintenance cost, Crep,jIt is replacement cost, j=1,
2 ..., 6 be component index, and T is the time of whole process experience, and unit is year.
In an example of the present invention, specific calculation formula is as follows,
Wherein, Ci,PVFor the cost of investment of photovoltaic power generation, COM,PVFor the operation and maintenance cost of photovoltaic power generation, Ci,WTFor wind
The cost of investment of power power generation, COM,WTFor the operation and maintenance cost of wind-power electricity generation, Ci,FCFor the cost of investment of fuel cell, COM,FC
For the operation and maintenance cost of fuel cell, COM,FCFor the cost of investment of electrolytic cell, COM,elecrFor the operation and maintenance of electrolytic cell
Cost, Ci,batFor the cost of investment of battery capacity,
COM,batFor the operation and maintenance cost of battery capacity；The unit of cost of investment is member, the list of operation and maintenance cost
Position is member/year.The cost of investment of hydrogen container is 7000 yuan/kg, and operation and maintenance cost are annual 35 yuan/kg；Lifetime of system is T
Interest rate when year is 10%.
The constraint condition of polymorphic energy consumption system is accordingly
SOCmin< SOC (t) < SOCmax
H2min< H2(t) < H2max
0≤x (j)≤rated value
Ej(t)≤Pj×Δt
LPSP < εLPSP
TEL < εTEL
TELSUB < εTELSUB
Wherein, Δ t is time interval (1h)；SOCminIt is the minimum charge volume of battery, SOCmaxIt is the maximum charge of battery
Amount, SOC (t) is the charge volume of battery t moment, need to be between minimum charging capacity and maximum charge capacity；H2minIt is minimum hydrogen
Gas capacity, H2maxIt is maximum hydrogen capacity, H2(t) be t moment hydrogen capacity, need to hold in minimum hydrogen capacity and maximum hydrogen
Between amount；X (j) is the power of photovoltaic system, need to be will be within rated value；Ej(t) be each generated energy of current time (such as
Photovoltaic power generation), power generation capacity P of the rated power within the unit time need to be not more thanj×Δt；LPSP is loss of supply probability,
TEL is total energy loss value, and TELSUB is the power difference between the capacity of the power supply device and energy storage device in electric system；
Preceding 4 constraint condition is the technological constraint that must satisfy in any possible solution, rear 3 constraint condition is can be excellent
Change the standard of processing, to find more satisfied solution.
S102: each norm constraint parameter is optimized.
Particularly as be after determining norm constraint parameter, that is, determine objective function after, to each norm constraint parameter into
Row optimization, preferably embodiment, optimizes each norm constraint parameter specifically: by shuffled frog leaping algorithm to mark
Quasi- constrained parameters optimize.Preferably embodiment determines energy storage device using each norm constraint parameter after optimization
Capacity after, further includes: be modified based on capacity of the ε-leash law to energy storage device.
Specifically, the first step is by the electricity in 3 standard loss of supply probability LPSP, total energy loss value TEL, electric system
The section amount of restraint ε of power difference TELSUB between source device and the capacity of energy storage device is divided into 1000 equal parts, as under
The ε value of the iterative calculation of the shuffled frog leaping algorithm SFLA in face.
Second step is that objective function is optimized according to system corresponding parameter, runs current SFLA most beutiful face first
Amount generates initial random variable and carries out the iterative calculation of fitness function, using the optimum of iterative calculation as reference
Signal calculates the error of the objective function optimized and optimum in current iteration, if error is constrained less than ε, keeps iteration
As a result and carry out next step, if error is constrained greater than ε, then need according to result more knots modification, to objective function carry out into
One-step optimization.
After saving iteration result, gradually increase the ε value of one of goal constraint function, and keeps other targets about
The ε value of beam function is constant, and calculated result is current optimal capacity, will if the ε value of all objective functions is also not up to maximum value
Current optimal capacity is input to the initial of second step and runs in the optimal capacity of SFLA, carries out the iteration optimization of a new round.
If the ε value of all objective functions all has reached the maximum value, then third step is carried out using norm criterion, according to institute
The iteration result of some preservations calculates the optimum capacity of the polymorphic energy consumption system based on ε leash law.
In practical applications, these norm constraint parameters can also be embedded by terminal objective function using penalty factor method
It is middle to be used as constraint condition.Different according to the range of related constraint function, the value of penalty factor is also different.Such as LPSP, TEL
5% can be taken as respectively with the desired value or target value of TELSUB, 6 × 107kWh and 8 × 103kWh.It is selected by testing
Corresponding penalty factor can obtain more suitable solution.
S103: the capacity of energy storage device is determined using each norm constraint parameter after optimization.
After determining each norm constraint parameter, and optimized to each norm constraint parameter, just with each after optimization
Norm constraint parameter determines the capacity of energy storage device.Preferably embodiment, energy storage device specifically includes and wind-power electricity generation
The corresponding Wind energy storage device of system and the corresponding solar energy storage device of photovoltaic generating system, with fuel cell correspondingization
Learning can energy storage device, electrolytic cell and air accumulator.
Chemical energy is directly combined into electric energy as DC power supply, the calculating of desired voltage Ec from hydrogen and oxygen by fuel cell
Formula are as follows:
Wherein, Tc refers to battery temperature,WithThe pressure of hydrogen and oxygen is respectively referred to, Ed, which refers to, considers H2And O2Between phase
The correction value mutually influenced, formula are as follows:
Wherein, λeRefer to invariant, i (t) refers to that battery current, * are convolution operator, τeRefer to the time delay of hydrogen-oxygen flowing.Combustion
The output voltage of material battery is determined that Fig. 4 is provided by the embodiment of the present invention by the polarization curve or V-I characteristic of nonlinearity
The output voltage of fuel cell and the graph of relation of current density, as shown in Figure 4.
The hydrogen-producing speed of electrolytic cell depends on current density.It, can be equivalent by electrolytic cell in an example of the present invention
At a simple circuit structure, including an internal dc voltage and a series of nonlinear resistances.Nonlinear resistance indicates electricity
The internal loss of slot is solved, it is the function of temperature and electric current.The voltage V at its both ends under the conditions of its no currentrefCalculation formula be
Wherein, V0Referring to that open-circuit voltage, R refer to that the general constant of perfect gas, T refer to temperature, F refers to Faraday constant,With
The pressure of hydrogen and oxygen is respectively referred to,Refer to water activity.
In practical applications, a kind of more accurate electric model, including activation voltage drop V can also be usedact, gas propagation
Voltage drop VprogWith ohmic voltage drop Vohmic。
Activate voltage drop VactIndicate the electrochemistry formated behavior of electrolytic cell, formula are as follows:
Wherein, α is referred to as electric charge transfer coefficient, in 0~1 range.Its value depends on related reaction and electrode
Material.I0Value can be regarded as voltage drop and become apparent electric current.
Gas propagation voltage drop VprogIt is voltage drop caused by causing reaction speed to reduce due to the propagation phenomenon of gas,
Formula are as follows:
Wherein, β is invariant, IlimIt is the current limitation that maximum gas is propagated.
The low-density and low boiling temperature of hydrogen keep its storage challenging, therefore hydrogen mainly uses height in practice
Pressure storage, the storage of extremely low geothermal liquid or metal hydride storage.The pressure P of hydrogen containerstoCalculation formula are as follows:
Wherein, TstoThe temperature of hydrogen, VstoRefer to tank volume, mstoRefer to the flow of hydrogen.
The capacity determining methods of energy storage device, are first depending in electric system in a kind of electric system provided by the present invention
Target component determine multiple norm constraint parameters；Then each norm constraint parameter is optimized again；After finally utilizing optimization
Each norm constraint parameter determine the capacity of energy storage device.It can be seen that the capacity determining methods are according to multiple after optimizing
Norm constraint parameter is determined the capacity of energy storage device, in the prior art only choose single standard constrained parameters piece
Face the capacity of energy storage device in electric system is determined and is compared, using this determination method, energy storage device can be improved
Capacity accuracy promotes the stable operation of electric system.
It is described in detail above for a kind of embodiment of the capacity determining methods of energy storage device in electric system,
The capacity determining methods of energy storage device in the electric system described based on the above embodiment, the embodiment of the invention also provides one kind
The capacity determining device of energy storage device in electric system corresponding with this method.Embodiment and method part due to device part
Embodiment correspond to each other, therefore the embodiment of device part please refer to method part embodiment description, which is not described herein again.
Fig. 5 forms signal for the capacity determining device of energy storage device in a kind of electric system provided by the embodiment of the present invention
Figure, as shown in figure 5, the capacity determining device includes the first determining module 501, optimization module 502 and the second determining module
503。
First determining module 501, for determining multiple norm constraint parameters according to the target component in electric system；
Optimization module 502, for being optimized to each norm constraint parameter；
Second determining module 503, for determining the capacity of energy storage device using each norm constraint parameter after optimization.
The capacity determining device of energy storage device, is first depending in electric system in a kind of electric system provided by the present invention
Target component determine multiple norm constraint parameters；Then each norm constraint parameter is optimized again；After finally utilizing optimization
Each norm constraint parameter determine the capacity of energy storage device.It can be seen that the capacity determining device is according to multiple after optimizing
Norm constraint parameter is determined the capacity of energy storage device, in the prior art only choose single standard constrained parameters piece
Face the capacity of energy storage device in electric system is determined and is compared, using this determining device, energy storage device can be improved
Capacity accuracy promotes the stable operation of electric system.
On the basis of the above embodiments, preferably embodiment, optimization module 202 are specifically used for through the mixing frog
Jump algorithm optimizes norm constraint parameter.
On the basis of the above embodiments, preferably embodiment, further includes:
Correction module, for being modified based on capacity of the ε-leash law to energy storage device.
It is described in detail above for a kind of embodiment of the capacity determining methods of energy storage device in electric system,
The capacity determining methods of energy storage device in the electric system described based on the above embodiment, the embodiment of the invention also provides one kind
The capacity of energy storage device determines equipment in electric system corresponding with this method.Embodiment and method part due to environment division
Embodiment correspond to each other, therefore the embodiment of environment division please refer to method part embodiment description, which is not described herein again.
Fig. 6 determines equipment composition signal for the capacity of energy storage device in a kind of electric system provided by the embodiment of the present invention
Figure, as shown in fig. 6, the capacity determines that equipment includes memory 601 and processor 602.
Memory 601, for storing computer program；
Processor 602 realizes electric system provided by any one above-mentioned embodiment for executing computer program
The step of capacity determining methods of middle energy storage device.
The capacity of energy storage device determines equipment in a kind of electric system provided by the present invention, is according to multiple after optimizing
Norm constraint parameter is determined the capacity of energy storage device, in the prior art only choose single standard constrained parameters piece
Face the capacity of energy storage device in electric system is determined and is compared, using this determination equipment, energy storage device can be improved
Capacity accuracy promotes the stable operation of electric system.
It is described in detail above for a kind of embodiment of the capacity determining methods of energy storage device in electric system,
The capacity determining methods of energy storage device in a kind of electric system described based on the above embodiment, the embodiment of the invention also provides
A kind of computer readable storage medium corresponding with this method.Due to the embodiment and method of computer readable storage medium part
Partial embodiment corresponds to each other, therefore the embodiment of computer readable storage medium part please refers to the embodiment of method part
Description, which is not described herein again.
A kind of computer readable storage medium is stored with computer program, computer journey on computer readable storage medium
Sequence is executed by processor to realize that the capacity of energy storage device in a kind of electric system provided by any one above-mentioned embodiment is true
The step of determining method.
A kind of computer readable storage medium provided by the present invention, processor can read in readable storage medium storing program for executing and store
Program, it can realize the capacity determination side of energy storage device in a kind of electric system provided by above-mentioned any one embodiment
Method is to be determined according to multiple norm constraint parameters after optimization to the capacity of energy storage device, and only selects in the prior art
It takes single standard constrained parameters unilaterally to be determined the capacity of energy storage device in electric system to compare, energy storage can be improved
The capacity accuracy of device, promotes the stable operation of electric system.
Above to capacity determining methods, device, equipment and the storage of energy storage device in electric system provided by the present invention
Medium is described in detail.With several examples, principle and implementation of the present invention are described herein, above
The explanation of embodiment is merely used to help understand method and its core concept of the invention；Meanwhile for the general skill of this field
Art personnel, according to the thought of the present invention, there will be changes in the specific implementation manner and application range, in conclusion this
Description should not be construed as limiting the invention, those skilled in the art, right under the premise of no creative work
Modification, equivalent replacement, the improvement etc. that the present invention is made, should be included in the application.
It should also be noted that, in the present specification, relational terms such as first and second and the like be used merely to by
One operation is distinguished with another operation, without necessarily requiring or implying there are any between these entities or operation
This actual relationship or sequence.Moreover, the similar word such as term " includes ", so that including the unit of a series of elements, equipment
Or system not only includes those elements, but also including other elements that are not explicitly listed, or further includes for this list
Member, equipment or the intrinsic element of system.
Claims (10)
1. the capacity determining methods of energy storage device in a kind of electric system characterized by comprising
Multiple norm constraint parameters are determined according to the target component in electric system；
Each norm constraint parameter is optimized；
The capacity of energy storage device is determined using each norm constraint parameter after optimization.
2. the capacity determining methods of energy storage device in electric system according to claim 1, which is characterized in that described to each
The norm constraint parameter optimizes specifically:
The norm constraint parameter is optimized by shuffled frog leaping algorithm.
3. the capacity determining methods of energy storage device in electric system according to claim 1, which is characterized in that the utilization
Each norm constraint parameter after optimization determines after the capacity of energy storage device, further includes:
It is modified based on capacity of the ε-leash law to the energy storage device.
4. the capacity determining methods of energy storage device in electric system according to claim 1, which is characterized in that the energy storage
Device specifically includes and the corresponding Wind energy storage device of wind generator system, solar energy storage corresponding with photovoltaic generating system fill
It sets, chemical energy energy storage device corresponding with fuel cell, electrolytic cell and air accumulator.
5. the capacity determining methods of energy storage device in electric system according to claim 4, which is characterized in that the standard
Constrained parameters include power supply device in loss of supply probability, total energy loss value and the electric system of electric system with
Power difference between the capacity of the energy storage device.
6. the capacity determining device of energy storage device in a kind of electric system characterized by comprising
First determining module, for determining multiple norm constraint parameters according to the target component in electric system；
Optimization module, for being optimized to each norm constraint parameter；
Second determining module, for determining the capacity of energy storage device using each norm constraint parameter after optimization.
7. the capacity determining device of energy storage device in electric system according to claim 6, which is characterized in that the optimization
Module is specifically used for optimizing the norm constraint parameter by shuffled frog leaping algorithm.
8. the capacity determining device of energy storage device in electric system according to claim 6, which is characterized in that further include:
Correction module, for being modified based on capacity of the ε-leash law to the energy storage device.
9. the capacity of energy storage device determines equipment in a kind of electric system characterized by comprising
Memory, for storing computer program；
Processor, for executing the computer program to realize in the electric system as described in claim 1 to 5 any one
The step of capacity determining methods of energy storage device.
10. a kind of computer readable storage medium, which is characterized in that be stored with computer on the computer readable storage medium
Program, the computer program are executed by processor to realize in the electric system as described in claim 1 to 5 any one and store up
The step of capacity determining methods of energy device.
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